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Modelling and managing urban water demand through smart meters: Benefits and challenges from current research and emerging trends
1. Modelling and managing urban water demand
through smart meters: Benefits and challenges
from current research and emerging trends
Andrea Cominola, Matteo Giuliani, Andrea Castelletti,
Dario Piga, Andrea E. Rizzoli
2. US
246.2
Urban population in millions
81%
Urban percentage
Mexico
84.392
77%
Colombia
34.3
73%
Brazil
162.6
85%
Argentina
35.6
90%
Ukraine
30.9
68%
Russia
103.6
73%
China
559.2Urban population in millions
42%Urban percentage
Turkey
51.1
68%
India
329.3
29%
Bangladesh
38.2
26%
Philippines
55.0
64%
Indonesia
114.1
50%
S Korea
39.0
81%
Japan
84.7
66%
Egypt
33.1
43%
S Africa
28.6
60%
Canada
26.3
Venezuela
26.0
Poland
23.9
Thailand
21.5
Australia
18.3
Netherlands
13.3
Peru
21.0
Saudi Arabia
20.9
Iraq
20.3
Vietnam
23.3
DR Congo
20.2
Algeria
22.0Morocco
19.4
Malaysia
18.1
Burma
16.5
Sudan
16.3
Chile
14.6
N Korea
14.1
Ethiopia
13.0
Uzbekistan
10.1
Tanzania
9.9
Romania
11.6
Ghana
11.3
Syria
10.2
Belgium
10.2
80%
94%
62%
33%
89%
81%
73%
81%
67%
27%
33%
65%
60%
69%
32%
43%
88%
62%
16%
37%
25%
54%
49%
51%
97%
Nigeria
68.6
50%
UK
54.0
90%
France
46.9
77%
Spain
33.6
77%
Italy
39.6
68%
Germany
62.0
75%
Iran
48.4
68%
Pakistan
59.3
36%
Cameroon
Angola
Ecuador
Ivory
Coast
Kazakh-
stan
Cuba
Afghan-
istan
Sweden
Kenya
Czech
Republic
9.5
9.3
8.7
8.6
8.6
8.5
7.8
7.6
7.6
7.4
Mozam-
bique
Hong
Kong
Belarus
Tunisia
Hungary
Greece
Israel
Guate-
mala
Portugal
Yemen
Dominican
Republic
Bolivia
Serbia &
Mont
Switzer-
land
Austria
Bulgaria
Mada-
gascar
Libya
Senegal
Jordan
Zimbabwe
Nepal
Denmark
Mali
Azerbaijan
Singapore
El
Salvador
Zambia
Uganda
Puerto
Rico
Paraguay
UAE
Benin
Norway
New
Zealand
Honduras
Haiti
Nicaragua
Guinea
Finland
Uruguay
Lebanon
Somalia
Sri Lanka
Cambodia
Slovakia
Costa Rica
Palestine
Kuwait
Togo
Chad
Burkina
Ireland
Croatia
Congo
Niger
Sierra Leone
Malawi
Panama
Turkmenistan
Georgia
Lithuania
Liberia
Moldova
Rwanda
Kyrgyzstan
Oman
Armenia
Bosnia
Tajikistan
CAR
Melanesia
Latvia
Mongolia
Albania
Jamaica
Macedonia
Mauritania Laos
Gabon
Botswana
Slovenia
Eritrea
Estonia
Gambia
Burundi
Papua New Guinea
Namibia
Mauritius
Guinea-Bissau
Lesotho E Timor
Bhutan
Swaziland
Trinidad & Tobago
The earth reaches a momentous
milestone: by next year, for the first time
in history, more than half its population
will be living in cities. Those 3.3 billion
people are expected to grow to 5 billion
by 2030 — this unique map of the world
shows where those people live now
At the beginning of the 20th
century, the world's urban
population was only 220
million, mainly in the west
By 2030, the towns and
cities of the developing
world will make up 80%
of urban humanity
The new urban world
Urban growth, 2005—2010
Predominantly urban
75% or over
Predominantly urban
50—74%
Predominantly rural
25—49% urban
Predominantly rural
0—24% urban
Cities over 10 million people
(greater urban area)
Key
Tokyo
33.4
Osaka
16.6
Seoul
23.2
Manila
15.4
Jakarta
14.9
Dacca
13.8
Bombay
21.3
Delhi
21.1 Calcutta
15.5
Karachi
14.8
Shanghai
17.3
Canton
14.5
Beijing
12.7
Moscow
13.4
Tehran
12.1
Cairo
15.9
Istanbul
11.7
London
12.0
Lagos
10.0
Mexico
City
22.1
New York
21.8
Sao Paulo
20.4
LA
17.9
Rio de
Janeiro
12.2
Buenos
Aires
13.5
3,307,950,000The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe
0.1%
Eastern Europe
-0.4%
Arab States
Latin America
& Caribbean North America
3.2%
2.4%
1.3%
2.8%
1.7%
1.3%
Urban population is growing
Source: United Nations Population Fund, 2007
3. 2000 2030 2050
+130%
Source: United Nations. Department of Economic and Social Affairs. Population Division, 2010
Leflaive, X., et al. (2012), "Water", in OECD, OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris.
Domesticwaterdemand
… and so urban water demand
41 megacities
worldwide
4. city/district scale
TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)
Water Demand Management Strategies (WDMS)
5. TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)
Water Demand Management Strategies (WDMS)
customized WDMS
?
8. UK_WATER SUPPLY UTILITY
15 million customers
2.6 Gl/day drinking water
3 billion $ revenue (2013-14)
water utilities
Stakeholders involved
9. water utilities economy
Stakeholders involved
2014 global water meter market worth $2.6 billion
2018 increased to just over $3 billion
Smart Water Networks Can Save Utilities Worldwide up to
$12.5 Billion Annually Concludes Global Survey and
White Paper Released by Sensus
14. quarterly / half yearly basis readings
1 kilolitre (=1m3)
Billed-based approach
Traditional water meters
15. Smart meters
Smart meters resolution: 72 pulses/L
(=72k pulses/m3 )
Data logging resolution: 5-10 s interval
Information on time-of-day for consumption
Smart water meters
18. Are smart meters really useful?
DEMAND MANAGEMENT
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
19. Including user modellingin the loop
CUSTOMIZED DEMAND
MANAGEMENT
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
USER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
20. Including user modellingin the loop
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
21. How do we build this model?
The main challenges
24. End-use water consumption characterization
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
MAIN CHALLENGES
_ Big data management -> Data resolution
_ Intrusiveness (single-point VS on-device sensors)
25. End-use water consumption characterization
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
MAIN CHALLENGES
_ Big data management -> Data resolution
_ Intrusiveness (single-point VS on-device sensors)
_ Costs
_ Privacy
26. Users’ psychographics
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment
27. Users’ psychographics
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment
SURVEYS CAMPAIGNS – MAIN CHALLENGES:
_ Intrusiveness
_ Data update in time -> temporary occupancy, devices upgrades, …
28. Users’ response to external stimuli and WDMS
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
29. meter type data resolution
end-use
disaggregation
user profiling
WDMS and program
duration
Users’ response to external stimuli and WDMS
31. meter type
end-use
disaggregationdata resolution
WDMS and program
duration
Users’ response to external stimuli and WDMS
MAIN CHALLENGES
_ Few studies experimenting/testing WDMS
_ Short term experiments -> No rebound effect
37. LONDON | UK
Thames Water water supply utility
15 million customers served
3 Million smart meters installed by 2030
LOCARNO | CH
Società Elettrica Sopracenerina
multi-utility smart metering
(water, energy, gas)
400 smart water meters installed
VALENCIA | ES
EMIVASA water supply utility
2 million customers served
490,000 water smart meters currently installed
650,000 water smart meters installed by end 2015
The use cases
38. Discussion
_ To which extent are smart meters useful?
_ Are utilities really interested in saving water?
_ Is social influence relevant for water efficient behaviours?
_ How to monitor WDMS effect on the long term?
_ Water price elasticity?
_ Water and energy nexus?
_ Can gamification support behavioural change?